基于模糊C均值聚类和改进相关向量机的变压器故障诊断

Fault diagnosis of transformer based on fuzzy C means clustering and improved relevance vector machine

为了提高电力变压器故障诊断准确率和单一相关向量机核函数存在的固有二分类属性及对预测分类鲁棒性弱的问题,提出基于模糊C均值聚类和改进相关向量机的变压器故障诊断模型。首先对样本进行模糊C均值聚类,然后再采用相关向量机的完全二叉树结构进行划分。相关向量机核函数采用组合高斯核函数和多项式核函数构造的混合核函数,并利用双子群果蝇算法对混合核函数参数进行优化。实验表明,相比单核函数、粒子群算法优化混合核函数参数,所提方法准确率高、稳定性好,同时分类速度快,满足实时在线故障诊断。

In order to improve the accuracy rate of fault diagnosis of power transformer and the inherent two classification attributes of a single relevance vector machine kernel function and the problem of weak robustness to prediction classification, a new model for fault diagnosis of transformer based on fuzzy C means clustering and the improved relevance vector machine is proposed in this paper. Firstly, the sample is clustered by fuzzy C means, and then, the sample is divided by complete binary tree structure of relevance vector machine. The kernel function of relevance vector machine is the mixed kernel function constructed by combining Gauss kernel function and Polynomial kernel function, and the parameters of the mixed kernel function are optimized by using the double subgroups FOA with the characteristics of levy flight. The experimental results show that, compared with single kernel function and particle swarm optimization for optimizing parameters of mixed kernel function, the proposed method has high accuracy, good stability and fast classification speed, and meets real-time online fault diagnosis.